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</style>
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</head>
<body class="article">
<div id="header">
<h1>Go 语言性能优化</h1>
</div>
<div id="content">
<div id="preamble">
<div class="sectionbody">
<div class="paragraph">
<p><a href="https://colin-scott.github.io/personal_website/research/interactive_latency.html">Latency numbers every programmer should know
</a></p>
</div>
</div>
</div>
<div class="sect1">
<h2 id="true优化工作流"><a class="anchor" href="#true优化工作流"></a>优化工作流</h2>
<div class="sectionbody">
<div class="paragraph">
<p>建立评估指标(eg. Latency) &#8594; 定位瓶颈(一般都会定位到某个局部) &#8594; 寻找局部解决问题方案 &#8594; 尝试方案</p>
</div>
<div class="paragraph">
<p>不断重复</p>
</div>
</div>
</div>
<div class="sect1">
<h2 id="true问题定位工具"><a class="anchor" href="#true问题定位工具"></a>问题定位工具</h2>
<div class="sectionbody">
<div class="sect2">
<h3 id="truepprof"><a class="anchor" href="#truepprof"></a>pprof</h3>
<div class="paragraph">
<p>基本原理：</p>
</div>
<div class="quoteblock">
<blockquote>
<div class="paragraph">
<p>The builtin Go CPU profiler uses the setitimer(2) system call to ask the operating system to be sent a SIGPROF signal 100 times a second. Each signal stops the Go process and gets delivered to a random thread&#8217;s sigtrampgo() function. This function then proceeds to call sigprof() or sigprofNonGo() to record the thread&#8217;s current stack.</p>
</div>
<div class="paragraph">
<p>Since Go uses non-blocking I/O, Goroutines that wait on I/O are parked and not running on any threads. Therefore they end up being largely invisible to Go&#8217;s builtin CPU profiler.</p>
</div>
</blockquote>
</div>
<div class="paragraph">
<p>每秒被唤醒 100 次，记录每个线程上的栈，那些等待 IO 被 gopark 之类挂起的 goroutine 不会被采集到，因为不在线程上运行，gopark 挂起 goroutine 后，当前线程一般会进 schedule &#8594; findrunnable 的调度循环。</p>
</div>
</div>
<div class="sect2">
<h3 id="truefgprof"><a class="anchor" href="#truefgprof"></a>fgprof</h3>
<div class="quoteblock">
<blockquote>
<div class="paragraph">
<p>fgprof is implemented as a background goroutine that wakes up 99 times per second and calls runtime.GoroutineProfile. This returns a list of all goroutines regardless of their current On/Off CPU scheduling status and their call stacks.</p>
</div>
</blockquote>
</div>
<div class="paragraph">
<p>比较类似，但是会包含那些 Off CPU 的 goroutine。比如可以结合该库与 goroutine 的增长情况来做一段逻辑：当 goroutine 突然增长时，用 fgprof 采样 x 秒，可以发现是在代码的什么位置发生了阻塞。当然，也可以直接把 pprof 的 goroutine stack 给 dump 下来。</p>
</div>
</div>
<div class="sect2">
<h3 id="truetrace"><a class="anchor" href="#truetrace"></a>trace</h3>
<div class="paragraph">
<p>一般用来诊断一些诡异的抖动问题，或 runtime 的 bug(或者用来学习 runtime 的执行流)，用来做问题诊断效果一般。</p>
</div>
<div class="paragraph">
<p>基本原理是在 runtime 中埋了大量点，记录一堆 event 来追踪 runtime 执行流程。</p>
</div>
<div class="paragraph">
<p>如果对一些调度问题有疑问，可以在 trace 里做观察，不过拿来定位问题还是比较费劲的。</p>
</div>
<div class="paragraph">
<p><a href="https://xargin.com/a-rlock-story/" class="bare">https://xargin.com/a-rlock-story/</a></p>
</div>
</div>
<div class="sect2">
<h3 id="trueperf"><a class="anchor" href="#trueperf"></a>perf</h3>
<div class="paragraph">
<p>perf 也是可以用的，比如线上没开 pprof 的时候，发现 CPU 炸了，perf 可以看看到底在干啥，因为 Go 默认会把 DWARF 调试信息带进二进制文件中，通过 perf 的 zoom 功能也可以一直看到哪行代码(或者是汇编)占用了比较高的 CPU。</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="text" class="language-text hljs">$ perf stat -e task-clock,cycles,instructions,cache-references,cache-misses ./hello
yyyy

 Performance counter stats for './hello':

          1.464376      task-clock (msec)         #    0.979 CPUs utilized
         3,681,288      cycles                    #    2.514 GHz
         1,722,170      instructions              #    0.47  insn per cycle
            46,475      cache-references          #   31.737 M/sec
            21,479      cache-misses              #   46.216 % of all cache refs

       0.001495925 seconds time elapsed</code></pre>
</div>
</div>
<div class="paragraph">
<p>perf top</p>
</div>
<div class="imageblock">
<div class="content">
<img src="perf.png" alt="perf">
</div>
</div>
</div>
</div>
</div>
<div class="sect1">
<h2 id="true局部优化"><a class="anchor" href="#true局部优化"></a>局部优化</h2>
<div class="sectionbody">
<div class="paragraph">
<p>go test -bench=. -benchmem</p>
</div>
<div class="paragraph">
<p>或者</p>
</div>
<div class="paragraph">
<p>go test -cpuprofile -bench</p>
</div>
<div class="paragraph">
<p>memprofile 同理，一次只 bench 一种，否则可能不准。</p>
</div>
</div>
</div>
<div class="sect1">
<h2 id="true全局优化"><a class="anchor" href="#true全局优化"></a>全局优化</h2>
<div class="sectionbody">
<div class="paragraph">
<p>寻找程序的整体瓶颈。</p>
</div>
<div class="paragraph">
<p>wrk、pprof、压测平台</p>
</div>
<div class="paragraph">
<p><a href="https://github.com/bojand/ghz" class="bare">https://github.com/bojand/ghz</a></p>
</div>
<div class="paragraph">
<p>有压测平台是最好的，方便 AB，自己玩比较容易手忙脚乱，数据错位(压测的时候收集数据写报告经常容易张冠李戴，导致返工，还是有平台安逸)。</p>
</div>
</div>
</div>
<div class="sect1">
<h2 id="true性能瓶颈举例"><a class="anchor" href="#true性能瓶颈举例"></a>性能瓶颈举例</h2>
<div class="sectionbody">
<div class="sect2">
<h3 id="true业务逻辑"><a class="anchor" href="#true业务逻辑"></a>业务逻辑</h3>
<div class="admonitionblock warning">
<table>
<tr>
<td class="icon">
<i class="fa icon-warning" title="Warning"></i>
</td>
<td class="content">
<div class="paragraph">
<p>调用外部命令</p>
</div>
</td>
</tr>
</table>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">package main

import (
	"os/exec"
	"testing"

	uuid "github.com/satori/go.uuid"
)

var uu []byte
var u1 uuid.UUID

func BenchmarkUUIDExec(b *testing.B) {
	for i := 0; i &lt; b.N; i++ {
		uu, _ = exec.Command("uuidgen").Output()
	}
}

func BenchmarkUUIDLib(b *testing.B) {
	for i := 0; i &lt; b.N; i++ {
		u1 = uuid.NewV4()
	}
}</code></pre>
</div>
</div>
</div>
<div class="sect2">
<h3 id="true序列化-cpu-占用过高"><a class="anchor" href="#true序列化-cpu-占用过高"></a>序列化 CPU 占用过高</h3>
<div class="paragraph">
<p>寻找一些针对性进行过优化的库，或者从文本协议更换为二进制协议。</p>
</div>
<div class="paragraph">
<p>比如 k8s 为了性能就集成了 jsoniter。</p>
</div>
</div>
<div class="sect2">
<h3 id="true算法时间复杂度"><a class="anchor" href="#true算法时间复杂度"></a>算法时间复杂度</h3>
<div class="paragraph">
<p>显而易见，O(logn) 和 O(n)，O(logn) 最多就 64 次，而 O(n) 可能耗尽计算资源。</p>
</div>
<div class="paragraph">
<p>runtime 里的算法优化：</p>
</div>
<div class="imageblock">
<div class="content">
<img src="runtime_opt.png" alt="runtime opt">
</div>
</div>
</div>
<div class="sect2">
<h3 id="true过多的系统调用"><a class="anchor" href="#true过多的系统调用"></a>过多的系统调用</h3>
<div class="paragraph">
<p>合并调用</p>
</div>
<div class="ulist">
<ul>
<li>
<p>如 writev，但是合并的 syscall 延迟可能会上升。</p>
</li>
<li>
<p>pipeline，一下发一堆请求，不过现在可能连 HTTP 的 pipeline 都不一定支持得好。经常被 benchmark 玩家用来刷数据。</p>
</li>
</ul>
</div>
</div>
<div class="sect2">
<h3 id="true过多的对象"><a class="anchor" href="#true过多的对象"></a>过多的对象</h3>
<div class="admonitionblock warning">
<table>
<tr>
<td class="icon">
<i class="fa icon-warning" title="Warning"></i>
</td>
<td class="content">
<div class="paragraph">
<p>字符串操作</p>
</div>
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>用加号连接，和 Sprintf 差别还是比较大的：</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">func BenchmarkBytesBufferAppend(b *testing.B) {
	for i := 0; i &lt; b.N; i++ {
		var msg bytes.Buffer
		msg.WriteString("userid : " + "1")
		msg.WriteString("location : " + "ab")
	}
}

func BenchmarkBytesBufferAppendSprintf(b *testing.B) {
	for i := 0; i &lt; b.N; i++ {
		var msg bytes.Buffer
		msg.WriteString(fmt.Sprintf("userid : %d", 1))
		msg.WriteString(fmt.Sprintf("location : %s", "ab"))
	}
}</code></pre>
</div>
</div>
<div class="imageblock">
<div class="content">
<img src="string_bench.png" alt="string bench">
</div>
</div>
<div class="paragraph">
<p>fmt.打印系列大部分会造成变量逃逸(interface 参数)。</p>
</div>
<div class="sect3">
<h4 id="truesync-pool"><a class="anchor" href="#truesync-pool"></a>sync.Pool</h4>
<div class="paragraph">
<p>sync.Pool 才能实现 zero garbage。benchmark 中的 0 alloc，其实是因为对象有复用，alloc 平均 &lt; 1。</p>
</div>
<div class="paragraph">
<p>struct 可以复用(p = Person{}，用零值覆盖一次就可以)，slice 可以复用(a = a[:0])，但 map 不太好复用(得把所有 kv 全清空才行，成本可能比新建一个还要高)。比如 fasthttp 里，把本来应该是 map 的 header 结构变成了 slice，牺牲一点查询速度，换来了复用的方便。</p>
</div>
<div class="paragraph">
<p>复用本身可能导致 bug，例如：</p>
</div>
<div class="ulist">
<ul>
<li>
<p>拿出时不 Reset，内含脏数据:</p>
</li>
<li>
<p>slice 缩容时，被缩掉对象如果不置 nil，是不会释放的</p>
</li>
<li>
<p>在 Put 回 Pool 时，不判断大小，导致了进程占内存越来越大(标准库发生过这样的问题，在用户看起来，整个进程占用的内存一直在上涨，像是泄露一样)</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>第二点可以看下面这张图理解一下：</p>
</div>
<div class="imageblock">
<div class="content">
<img src="subslice.png" alt="subslice">
</div>
</div>
<div class="paragraph">
<p>a = a[:1]，如果后面的元素都是指针，都指向了 500MB 的一个大 buffer，没法释放，GC 认为你还是持有引用的。这种情况需要自己先把后面的元素全置为 nil，再缩容。</p>
</div>
</div>
<div class="sect3">
<h4 id="trueoffheap"><a class="anchor" href="#trueoffheap"></a>offheap</h4>
<div class="paragraph">
<p>如果数据不可变，只作查询，也可以考虑 offheap，但局限性较大。</p>
</div>
<div class="paragraph">
<p>下面三个库可以看看。</p>
</div>
<div class="paragraph">
<p><a href="https://github.com/glycerine/offheap" class="bare">https://github.com/glycerine/offheap</a></p>
</div>
<div class="paragraph">
<p><a href="https://github.com/coocood/freecache" class="bare">https://github.com/coocood/freecache</a></p>
</div>
<div class="paragraph">
<p><a href="https://github.com/allegro/bigcache" class="bare">https://github.com/allegro/bigcache</a></p>
</div>
<div class="paragraph">
<p>最近 dgraph 有一篇[分享](<a href="https://dgraph.io/blog/post/manual-memory-management-golang-jemalloc/)，用" class="bare">https://dgraph.io/blog/post/manual-memory-management-golang-jemalloc/)，用</a> jemalloc 和封装的 cgo 方法，可以把一些 hotpath 上分配的对象放在堆外，这个库的局限是在堆外分配的对象不能引用任何 Go 内部的对象，否则可能破坏 GC 时的引用关系。</p>
</div>
<div class="paragraph">
<p>理论上一些 QPS 较低，但每次请求很大的系统，或许可以参考这个库，把 buffer 放在堆外。</p>
</div>
</div>
<div class="sect3">
<h4 id="true减少指针类型变量逃逸"><a class="anchor" href="#true减少指针类型变量逃逸"></a>减少指针类型变量逃逸</h4>
<div class="paragraph">
<p>使用 go build -gcflags="-m -m" 来分析逃逸。</p>
</div>
<div class="paragraph">
<p>如果要分析某个 package 内的逃逸情况，可以打全 package 名，例如 go build -gcflags="-m -m" github.com/cch123/elasticsql</p>
</div>
<div class="paragraph">
<p>string 类型天然就是带指针的类型，比如一些 cache 服务，有几千万 entry，那么用 string 来做 key 和 value 可能成本就很高。</p>
</div>
<div class="admonitionblock tip">
<table>
<tr>
<td class="icon">
<i class="fa icon-tip" title="Tip"></i>
</td>
<td class="content">
<div class="paragraph">
<p>减少指针的手段：</p>
</div>
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>用值类型代替指针类型，比如：</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">*int -&gt; struct {value int, isNull bool}

string -&gt; struct {value [12]byte, length int)

数值类型的 string -&gt; int

*Host -&gt; Host</code></pre>
</div>
</div>
<div class="admonitionblock tip">
<table>
<tr>
<td class="icon">
<i class="fa icon-tip" title="Tip"></i>
</td>
<td class="content">
<div class="paragraph">
<p>减少逃逸的手段</p>
</div>
</td>
</tr>
</table>
</div>
<div class="ulist">
<ul>
<li>
<p>尽量少用 fmt.Print、fmt.Sprint 系列的函数。</p>
</li>
<li>
<p>设计函数签名时，参数尽量少用 interface</p>
</li>
<li>
<p>少用闭包，被闭包引用的局部变量会逃逸到堆上</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>不过这些也就说说而已，真的每一条都遵循怕是写代码的时候已经疯了。况且 Go 的 defer 只能在函数作用域内运作，为了避免 panic 死锁，很多时候套个闭包的操作还是比较常见的。</p>
</div>
</div>
<div class="sect3">
<h4 id="truemap-结构的-128-阈值"><a class="anchor" href="#truemap-结构的-128-阈值"></a>map 结构的 128 阈值</h4>
<div class="paragraph">
<p>key &gt; 128 字节时，indirectkey = true</p>
</div>
<div class="paragraph">
<p>value &gt; 128 字节时，indirectvalue = true</p>
</div>
<div class="paragraph">
<p>我们可以用 lldb 来进行简单验证:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">package main

import "fmt"

func main() {
    type P struct {
        Age [16]int
    }
    var a = map[P]int{}
    a[P{}] = 1
    fmt.Println(a)
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>在 lldb 中可以看到 indirectkey 为 false。</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="text" class="language-text hljs">(lldb) b mapassign
(lldb) p *t
(runtime.maptype) *t = {
  typ = {
    size = 0x0000000000000008
    ptrdata = 0x0000000000000008
    hash = 2678392653
    tflag = 2
    align = 8
    fieldalign = 8
    kind = 53
    alg = 0x0000000001137020
    gcdata = 0x00000000010cf298
    str = 26128
    ptrToThis = 0
  }
  key = 0x00000000010a77a0
  elem = 0x000000000109d180
  bucket = 0x00000000010aea00
  hmap = 0x00000000010b4da0
  keysize = 128  =======&gt; 128 字节
  indirectkey = false =====&gt; false
  valuesize = 8
  indirectvalue = false
  bucketsize = 1104
  reflexivekey = true
  needkeyupdate = false
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>现在 lldb 不支持 Golang 了，在 gdb 或者 dlv 里应该也可以看到这个字段。</p>
</div>
</div>
</div>
<div class="sect2">
<h3 id="true过多的调度-cpu-占用例如火焰图中schedule-有一大条"><a class="anchor" href="#true过多的调度-cpu-占用例如火焰图中schedule-有一大条"></a>过多的调度 CPU 占用(例如火焰图中，schedule 有一大条)</h3>
<div class="paragraph">
<p>类似 fasthttp 的 workerpool。</p>
</div>
<div class="paragraph">
<p><a href="https://github.com/valyala/fasthttp/blob/master/workerpool.go#L19">worker pool in fasthttp</a></p>
</div>
<div class="paragraph">
<p>创建好的 goroutine 可以反复使用，并且自己实现可以控制最大的并发 worker 数。</p>
</div>
</div>
<div class="sect2">
<h3 id="true锁冲突"><a class="anchor" href="#true锁冲突"></a>锁冲突</h3>
<div class="paragraph">
<p>通过阶梯加压，观察 goroutine 的变化趋势。当触发锁瓶颈时，会出现大量等锁的 goroutine。</p>
</div>
<div class="sect3">
<h4 id="true原因"><a class="anchor" href="#true原因"></a>原因</h4>
<div class="paragraph">
<p>临界区太大，其中包含系统调用。</p>
</div>
<div class="paragraph">
<p>有些锁是避免不了的，例如 fs.Write，一定有锁，且该锁在 runtime 内部。</p>
</div>
<div class="paragraph">
<p>性能敏感场合，全局锁，比如 rand 的全局锁。单机 10w+ QPS 即可能触发该瓶颈(和环境以及程序行为有关)</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">type lockedSource struct {
	lk  sync.Mutex
	src Source64
}

func (r *lockedSource) Int63() (n int64) {
	r.lk.Lock()
	n = r.src.Int63()
	r.lk.Unlock()
	return
}

func (r *lockedSource) Uint64() (n uint64) {
	r.lk.Lock()
	n = r.src.Uint64()
	r.lk.Unlock()
	return
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>有些开源库设计是一个 struct 对应一个 sync.Pool，这种时候，如果你不对该 struct 进行复用，就会触发 runtime 中的锁冲突：</p>
</div>
<div class="paragraph">
<p>参考本文中的第一个案例：</p>
</div>
<div class="paragraph">
<p><a href="https://xargin.com/lock-contention-in-go/">lock contention</a></p>
</div>
</div>
<div class="sect3">
<h4 id="true解决方案"><a class="anchor" href="#true解决方案"></a>解决方案</h4>
<div class="ulist">
<ul>
<li>
<p>map &#8594; sync.Map(读多写少)</p>
</li>
<li>
<p>换用无锁结构，如 lock free queue、stack 等</p>
</li>
<li>
<p>分段锁</p>
</li>
<li>
<p>copy on write，业务逻辑允许的前提下，在修改时拷贝一份，再修改</p>
</li>
</ul>
</div>
</div>
</div>
<div class="sect2">
<h3 id="true程序局部性"><a class="anchor" href="#true程序局部性"></a>程序局部性</h3>
<div class="sect3">
<h4 id="truefalse-sharing"><a class="anchor" href="#truefalse-sharing"></a>false sharing</h4>
<div class="paragraph">
<p>时间局部性、空间局部性</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">var semtable [semTabSize]struct {
	root semaRoot
	pad  [cpu.CacheLinePadSize - unsafe.Sizeof(semaRoot{})]byte
}</code></pre>
</div>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">var timers [timersLen]struct {
	timersBucket

	// The padding should eliminate false sharing
	// between timersBucket values.
	pad [cpu.CacheLinePadSize - unsafe.Sizeof(timersBucket{})%cpu.CacheLinePadSize]byte
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>类似下面的二维数组，怎么遍历更快？</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">var a = [10000][10000]int{}</code></pre>
</div>
</div>
<div class="paragraph">
<p>在标准库中，考虑到局部性而实现的 sort 的例子：</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">func quickSort_func(data lessSwap, a, b, maxDepth int) {
	for b-a &gt; 12 {
		if maxDepth == 0 {
			heapSort_func(data, a, b)
			return
		}
		maxDepth--
		mlo, mhi := doPivot_func(data, a, b)
		if mlo-a &lt; b-mhi {
			quickSort_func(data, a, mlo, maxDepth)
			a = mhi
		} else {
			quickSort_func(data, mhi, b, maxDepth)
			b = mlo
		}
	}
	if b-a &gt; 1 {
		for i := a + 6; i &lt; b; i++ {
			if data.Less(i, i-6) {
				data.Swap(i, i-6)
			}
		}
		insertionSort_func(data, a, b)
	}
}</code></pre>
</div>
</div>
</div>
<div class="sect3">
<h4 id="truetrue-sharing"><a class="anchor" href="#truetrue-sharing"></a>true sharing</h4>
<div class="paragraph">
<p>这时候一般都有锁，所以本质上还是怎么降低锁的粒度。</p>
</div>
<div class="quoteblock">
<blockquote>
<div class="paragraph">
<p>sync: RWMutex scales poorly with CPU count</p>
</div>
</blockquote>
</div>
</div>
</div>
<div class="sect2">
<h3 id="truetimer-性能问题"><a class="anchor" href="#truetimer-性能问题"></a>timer 性能问题</h3>
<div class="ulist">
<ul>
<li>
<p>老版本的 timer 会有高压力下触发不准时问题，且触发大量的 syscall &#8594; <a href="https://github.com/golang/go/issues/25471">Go issue 25471</a></p>
</li>
</ul>
</div>
<div class="listingblock">
<div class="content">
<pre>// xiaorui.cc

go1.13

% time     seconds  usecs/call     calls    errors syscall
------ ----------- ----------- --------- --------- ----------------
 84.00   12.007993         459     26148      3874 futex
 11.43    1.634512         146     11180           nanosleep
  4.45    0.635987          32     20185           sched_yield

go1.14

% time     seconds  usecs/call     calls    errors syscall
------ ----------- ----------- --------- --------- ----------------
 58.78    4.837332         174     27770      4662 futex
 19.50    1.605189         440      3646           nanosleep
 11.55    0.950730          44     21569           epoll_pwait
  9.75    0.802715          36     22181           sched_yield:w</pre>
</div>
</div>
<div class="paragraph">
<p>优化后，CPU 占用降低，到时不触发的问题也有所改善。更具体的可以参考 <a href="http://xiaorui.cc/archives/6483">这篇文章</a>。</p>
</div>
<div class="ulist">
<ul>
<li>
<p>用时间轮实现粗粒度的时间库</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>可以搜搜大量的 timewheel 库。</p>
</div>
<div class="paragraph">
<p>ticker 使用时要尤其注意泄露问题，否则程序 CPU 使用会逐渐上涨：</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">package main

import (
    "fmt"
    "time"
)

func main() {
    for {
        select {
        case t := &lt;-time.Tick(time.Second * 2):
            fmt.Println(t, "hello world")
        }
    }
}</code></pre>
</div>
</div>
</div>
<div class="sect2">
<h3 id="true汇编优化"><a class="anchor" href="#true汇编优化"></a>汇编优化</h3>
<div class="paragraph">
<p>SIMD 优化，如 math 库。gonum 中也有一些例子。
无法跨平台，如未来碰到国产化需求要上 ARM、龙芯(MIPS) 就尴尬了。</p>
</div>
<div class="paragraph">
<p><a href="https://github.com/gonum/gonum/tree/master/internal/asm/f64">gonum 中的汇编优化</a></p>
</div>
</div>
</div>
</div>
<div class="sect1">
<h2 id="true语言本身的一些缺陷"><a class="anchor" href="#true语言本身的一些缺陷"></a>语言本身的一些缺陷</h2>
<div class="sectionbody">
<div class="sect2">
<h3 id="true越压越差"><a class="anchor" href="#true越压越差"></a>越压越差</h3>
<div class="paragraph">
<p>runtime 虽然会对 g 结构体进行 cache 和复用，但在瞬时的高峰时，可能产生同时在执行的大量 goroutine，这些 goroutine 会导致：</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">var (
	allgs    []*g
	allglock mutex
)</code></pre>
</div>
</div>
<div class="paragraph">
<p>runtime 中上面的 allgs 这个数组变大，比如你并发产生了 100000 个 goroutine，那么在高峰过后，即使不再服务任何请求，你的进程占用的 CPU 也会比高峰之前多。</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">package main

import (
	"log"
	"net/http"
	_ "net/http/pprof"
	"time"
)

func sayhello(wr http.ResponseWriter, r *http.Request) {}

func main() {
	for i := 0; i &lt; 1000000; i++ {
		go func() {
			time.Sleep(time.Second * 10)
		}()
	}
	http.HandleFunc("/", sayhello)
	err := http.ListenAndServe(":9090", nil)
	if err != nil {
		log.Fatal("ListenAndServe:", err)
	}
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>可以在你的机器上跑跑上面这个程序，用下面的 python 脚本来验证：</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="python" class="language-python hljs">import psutil
import time

p = psutil.Process(1) # 改成你自己的 pid 就行了

while 1:
    v = str(p.cpu_percent())
    if "0.0" != v:
        print(v, time.time())
    time.sleep(1)</code></pre>
</div>
</div>
</div>
<div class="sect2">
<h3 id="true调度和锁"><a class="anchor" href="#true调度和锁"></a>调度和锁</h3>
<div class="paragraph">
<p>调度 + 锁出问题，难复现，难定位。</p>
</div>
<div class="paragraph">
<p>比如高压力下，刚拿到锁就被调度走了，且导致大量的其它需要获取锁的 goroutine 阻塞。</p>
</div>
<div class="quoteblock">
<blockquote>
<div class="paragraph">
<p>Authors of parallel programs have known for decades that performance can suffer badly if a thread is preempted while holding a lock; this is sometimes referred to as inopportune preemption.</p>
</div>
</blockquote>
</div>
</div>
<div class="sect2">
<h3 id="true不注意造成死循环会让整个进程-hang-住"><a class="anchor" href="#true不注意造成死循环会让整个进程-hang-住"></a>不注意造成死循环会让整个进程 hang 住</h3>
<div class="paragraph">
<p>GC 需要抢占所有 goroutine，老版本的抢占需要用户协程在 morestack 时主动退出。</p>
</div>
<div class="paragraph">
<p>卡 gcwaiting。</p>
</div>
<div class="paragraph">
<p>perf top 可解。</p>
</div>
</div>
<div class="sect2">
<h3 id="true物理机负载高时延迟非线性增长"><a class="anchor" href="#true物理机负载高时延迟非线性增长"></a>物理机负载高时，延迟非线性增长</h3>
<div class="paragraph">
<p>压力高会导致响应慢，响应慢会导致并发执行的 goroutine 数变多，响应结束后的垃圾变多，同时会导致更高的调度成本和 GC 扫描成本，级联恶化。</p>
</div>
</div>
<div class="sect2">
<h3 id="true调度导致-cpu-密集型业务超时"><a class="anchor" href="#true调度导致-cpu-密集型业务超时"></a>调度导致 CPU 密集型业务超时</h3>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="c" class="language-c hljs">package main

import "fmt"
import "golang.org/x/crypto/bcrypt"
import "time"

func test(cost int, id int) {
	startTime := time.Now()
	code, _ := bcrypt.GenerateFromPassword([]byte("password"), cost)
	fmt.Println(time.Since(startTime), "END test ", id, code[0])
}

func main() {
	startTime := time.Now()
	code, _ := bcrypt.GenerateFromPassword([]byte("password"), 15)
	fmt.Println(time.Since(startTime), "END test ", -1, code[0], "\n")

	// 修改 4 为不同的值，猜猜结果？
	for i := 0; i &lt; 4; i++ {
		go test(15, i)
	}
	time.Sleep(1e16)
}</code></pre>
</div>
</div>
</div>
<div class="sect2">
<h3 id="true老版本的问题"><a class="anchor" href="#true老版本的问题"></a>老版本的问题</h3>
<div class="sect3">
<h4 id="truesync-pool-在-gc-时全清空"><a class="anchor" href="#truesync-pool-在-gc-时全清空"></a>sync.Pool 在 GC 时全清空</h4>
<div class="paragraph">
<p>导致在每一轮 GC 后都有延迟抖动，升级 1.13 后长尾请求有改善。</p>
</div>
<div class="paragraph">
<p>sync.Pool 的设计思路：尽量从本地拿到 cache 对象，拿不到通过无锁 shared 队列去找，还是找不到，全局 lock 找或者生成新的。</p>
</div>
<div class="paragraph">
<p>这种思路比较类似 L1 &#8594; L2 &#8594; L3 的多级缓存设计，runtime 的内存分配演进也是类似的思路。</p>
</div>
</div>
</div>
<div class="sect2">
<h3 id="true当前问题定位工具的局限性"><a class="anchor" href="#true当前问题定位工具的局限性"></a>当前问题定位工具的局限性</h3>
<div class="paragraph">
<p>难以定位抖动问题。</p>
</div>
<div class="paragraph">
<p>无论 pprof、perf、fgprof、trace 都是人肉触发，抖动时人又不在系统旁边。</p>
</div>
<div class="paragraph">
<p>这种情况需要 self-aware 的 profile dump 方式来解决问题。</p>
</div>
<div class="paragraph">
<p>或者向 Google 看齐:</p>
</div>
<div class="sect3">
<h4 id="truecontinuous-profiling"><a class="anchor" href="#truecontinuous-profiling"></a>continuous profiling</h4>
<div class="paragraph">
<p>在生产环境对更细粒度的程序性能做实时监控，方便及时发现、定位、分析问题。</p>
</div>
<div class="paragraph">
<p>早发现，早治疗，晚发现，成本高。</p>
</div>
<div class="paragraph">
<p><a href="https://storage.googleapis.com/pub-tools-public-publication-data/pdf/36575.pdf">google 论文</a></p>
</div>
<div class="imageblock">
<div class="content">
<img src="con_perf.png" alt="con perf">
</div>
</div>
</div>
</div>
</div>
</div>
<div class="sect1">
<h2 id="true补充知识"><a class="anchor" href="#true补充知识"></a>补充知识</h2>
<div class="sectionbody">
<div class="sect2">
<h3 id="true怎么阅读-pprof-的文本格式的-dump"><a class="anchor" href="#true怎么阅读-pprof-的文本格式的-dump"></a>怎么阅读 pprof 的文本格式的 dump</h3>
<div class="paragraph">
<p>下面是 heap profile 的 dump 结果，我们需要重点关心的是里面的几个数值：</p>
</div>
<hr>
<div class="paragraph">
<p>94(94 个 inuse objects): 6160384(6MB inuse space) [5828(5828 个 alloc objects): 381943808(381M 的 alloc space)] @ 0x1370f09 0x1397394 0x1396b32 0x1395e65 0x13981ab 0x13a313a 0x13a9c60 0x13a9401 0x13b4125 0x1703b69 0x1703b0b 0x170cdd1 0x170fcb7 0x170f90d 0x174e369 0x1510930 0x150b5a5 0x17ad46c 0x179fc0e 0x17b1958 0xf43e70 0x49b481
<em>_</em></p>
</div>
<div class="paragraph">
<p>后面这些指的是程序的代码地址，Go 可以根据这些地址在 .text 段中找到具体的代码位置。</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code data-lang="go" class="language-go hljs">---
profile : heap profile: 1240: 51385648 [50587184: 280604213336] @ heap/1048576
94: 6160384 [5828: 381943808] @ 0x1370f09 0x1397394 0x1396b32 0x1395e65 0x13981ab 0x13a313a 0x13a9c60 0x13a9401 0x13b4125 0x1703b69 0x1703b0b 0x170cdd1 0x170fcb7 0x170f90d 0x174e369 0x1510930 0x150b5a5 0x17ad46c 0x179fc0e 0x17b1958 0xf43e70 0x49b481
#    0x1370f08       gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client/async.NewAsyncClient+0x208                      /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client/async/async_client.go:55
#  0x1397393       gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client.(*ClientPool).newAsyncClient+0x73               /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client/client_pool.go:110
#        0x1396b31       gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client.(*ClientPool).takeClient+0x3f1                  /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client/client_pool.go:66
# 0x1395e64       gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client.(*ClientHandler).takeClient+0x1b4               /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client/client_handler.go:46
#      0x13981a gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client.(*CommandExecutor).do+0x1ba                     /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client/command_executor.go:58
#    0x13a313 gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client.(*TBaseClient).Do+0x99                          /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/oooo/redis-go-client/redis_client.go:73
#        0x13a9c5 gitlab.outer.com/ooh-mesh/mosn/pkg/upstream/zcache/client.(*RedisClientInterceptor).Do+0x13f                                        /go/src/gitlab.outer.com/ooh-mesh/mosn/pkg/upstream/zcache/client/redis_client_interceptor.go:29
#      0x13a9400       gitlab.outer.com/ooh-mesh/mosn/pkg/upstream/zcache/client.(*RedisClientDelegate).GetTsEx+0x120                                      /go/src/gitlab.outer.com/ooh-mesh/mosn/pkg/upstream/zcache/client/redis_client_delegate.go:33
# 0x13b4124       gitlab.outer.com/ooh-mesh/mosn/pkg/upstream/zcache/cachemanager.(*TBaseCacheManager).GetTsEx+0x1c4                                  /go/src/gitlab.outer.com/ooh-mesh/mosn/pkg/upstream/zcache/cachemanager/redis_cache_manager.go:96
#     0x1703b68       gitlab.outer.com/ooh-mesh/mosn/pkg/upstream/zcache/cachemanager.(*RefreshableTBaseCacheManager).GetTsEx+0x98                        /go/src/gitlab.outer.com/ooh-mesh/mosn/pkg/upstream/zcache/cachemanager/refreshable_redis_cache_manager.go:55
# 0x1703b0a       gitlab.outer.com/ooh-mesh/mosn/pkg/gateway/integrate.(*ZCacheClient).GetObjectLDC+0x3a                                              /go/src/gitlab.outer.com/ooh-mesh/mosn/pkg/gateway/integrate/zcacheclient.go:67
#       0x170cdd0       gitlab.outer.com/ooh-mesh/mosn/pkg/gateway/session.queryAlipaySessionById+0x790                                                     /go/src/gitlab.outer.com/ooh-mesh/mosn/pkg/gateway/session/mobile_session_manager.go:42
#       0x170fcb6       gitlab.outer.com/ooh-mesh/mosn/pkg/gateway/session.getAlipaySession+0x186                                                           /go/src/gitlab.outer.com/ooh-mesh/mosn/pkg/gateway/session/session_manager.go:35
#      0x170f90c       gitlab.outer.com/ooh-mesh/mosn/pkg/gateway/session.InitSession+0x3c                        /go/src/gitlab.outer.com/ooh-mesh/mosn/pkg/gateway/session/session_manager.go:14
#      0x174e368       gitlab.outer.com/ooh-mesh/mosn/pkg/gateway/handler.(*SessionHandler).HandleIn+0x618                                         /go/src/gitlab.outer.com/ooh-mesh/mosn/pkg/gateway/handler/session_handler.go:32
#      0x151092f       gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/ooh-mesh/sofa-mosng/pkg/gateway.(*DefaultPipeline).RunInHandlers+0xcf   /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/ooh-mesh/sofa-mosng/pkg/gateway/pipeline.go:45
#    0x150b5a4       gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/ooh-mesh/sofa-mosng/pkg/gateway.(*GatewayFilter).OnReceive+0x3c4        /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/gitlab.outer.com/ooh-mesh/sofa-mosng/pkg/gateway/filter.go:89
#      0x17ad46b       gitlab.outer.com/ooh-mesh/mosn/vendor/mosn.io/mosn/pkg/proxy.(*downStream).runReceiveFilters+0xdb                                   /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/mosn.io/mosn/pkg/proxy/streamfilters.go:50
#      0x179fc0d       gitlab.outer.com/ooh-mesh/mosn/vendor/mosn.io/mosn/pkg/proxy.(*downStream).receive+0x24d                                            /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/mosn.io/mosn/pkg/proxy/downstream.go:404
#        0x17b1957       gitlab.outer.com/ooh-mesh/mosn/vendor/mosn.io/mosn/pkg/proxy.(*downStream).OnReceive.func1+0xd7                                     /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/mosn.io/mosn/pkg/proxy/downstream.go:373
#        0xf43e6f        gitlab.outer.com/ooh-mesh/mosn/vendor/mosn.io/mosn/pkg/sync.(*workerPool).spawnWorker+0x4f                                          /go/src/gitlab.outer.com/ooh-mesh/mosn/vendor/mosn.io/mosn/pkg/sync/workerpool.go:169</code></pre>
</div>
</div>
<hr>
<div class="paragraph">
<p>推荐材料：</p>
</div>
<div class="paragraph">
<p><a href="http://team.jiunile.com/blog/2020/05/go-performance.html">一篇中文总结</a></p>
</div>
<div class="paragraph">
<p><a href="https://dave.cheney.net/high-performance-go-workshop/dotgo-paris.html">go performance workshop</a></p>
</div>
<div class="paragraph">
<p><a href="https://github.com/dgryski/go-perfbook/blob/master/performance.md">go-perfbook</a></p>
</div>
<div class="paragraph">
<p><a href="https://github.com/cch123/knowledge-index/blob/master/high_perf/Performance%20optimization_%20Pros%20%26%20Cons.pdf">fasthttp 作者的性能优化分享</a></p>
</div>
<div class="paragraph">
<p><a href="https://github.com/cch123/knowledge-index/blob/master/high_perf/Performance_optimization_sins_-_Aliaksandr_Valialkin.pdf">fasthttp 作者的性能优化分享2</a></p>
</div>
</div>
</div>
</div>
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Last updated 2020-12-04 15:09:54 +0800
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